src/api/schemas.py

from pydantic import BaseModel, Field


class TransactionInput(BaseModel):
    Time: float = Field(..., description="Seconds elapsed since the first transaction in the dataset")
    V1:   float; V2:  float; V3:  float; V4:  float; V5:  float
    V6:   float; V7:  float; V8:  float; V9:  float; V10: float
    V11:  float; V12: float; V13: float; V14: float; V15: float
    V16:  float; V17: float; V18: float; V19: float; V20: float
    V21:  float; V22: float; V23: float; V24: float; V25: float
    V26:  float; V27: float; V28: float
    Amount: float = Field(..., description="Transaction amount in dollars", ge=0)

    model_config = {
        "json_schema_extra": {
            "example": {
                "Time": 0.0,
                "V1": -1.3598, "V2": -0.0728, "V3": 2.5363, "V4": 1.3782,
                "V5": -0.3383, "V6": 0.4624, "V7": 0.2396, "V8": 0.0987,
                "V9": 0.3638, "V10": 0.0908, "V11": -0.5516, "V12": -0.6178,
                "V13": -0.9914, "V14": -0.3112, "V15": 1.4681, "V16": -0.4704,
                "V17": 0.2080, "V18": 0.0258, "V19": 0.4040, "V20": 0.2514,
                "V21": -0.0183, "V22": 0.2778, "V23": -0.1105, "V24": 0.0669,
                "V25": 0.1285, "V26": -0.1892, "V27": 0.1336, "V28": -0.0211,
                "Amount": 149.62
            }
        }
    }


class PredictionOutput(BaseModel):
    is_fraud:          bool
    fraud_probability: float = Field(..., description="Probability of fraud between 0 and 1")
    inference_ms:      float = Field(..., description="Time taken to run the prediction in milliseconds")